Spaces:
Sleeping
Sleeping
File size: 3,165 Bytes
0bbf6ef e91a768 0380162 b0e8452 3cadd69 fdedf54 0bbf6ef 43d306c e91a768 43d306c ec2e6e8 c65777e e91a768 c65777e e91a768 b296597 3cadd69 03c8fc6 3cadd69 c65777e 3cadd69 ec2e6e8 03c8fc6 ec2e6e8 03c8fc6 ec2e6e8 03c8fc6 ec2e6e8 03c8fc6 89415f2 e91a768 0bbf6ef e91a768 6c215ad e91a768 0bbf6ef 03c8fc6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import gradio as gr
from ultralytics import YOLO
import numpy as np
import fitz # PyMuPDF
import spaces
# Load the trained model
model_path = 'best.pt' # Replace with the path to your trained .pt file
model = YOLO(model_path)
# Define the class indices for figures and tables
figure_class_index = 3 # class index for figures
table_class_index = 4 # class index for tables
# Function to perform inference on an image and return bounding boxes for figures and tables
def infer_image_and_get_boxes(image, confidence_threshold=0.6):
results = model(image)
boxes = [
(int(box.xyxy[0][0]), int(box.xyxy[0][1]), int(box.xyxy[0][2]), int(box.xyxy[0][3]))
for result in results for box in result.boxes
if int(box.cls[0]) in {figure_class_index, table_class_index} and box.conf[0] > confidence_threshold
]
return boxes
# Function to crop images from the boxes
def crop_images_from_boxes(image, boxes, scale_factor):
cropped_images = [
image[int(y1 * scale_factor):int(y2 * scale_factor), int(x1 * scale_factor):int(x2 * scale_factor)]
for (x1, y1, x2, y2) in boxes
]
return cropped_images
@spaces.GPU
def process_pdf(pdf_file, chunk_size=10):
# Open the PDF file
doc = fitz.open(pdf_file)
all_cropped_images = []
# Set the DPI for inference and high resolution for cropping
low_dpi = 50
high_dpi = 300
# Calculate the scaling factor
scale_factor = high_dpi / low_dpi
# Process the PDF in chunks
num_pages = len(doc)
for chunk_start in range(0, num_pages, chunk_size):
chunk_end = min(chunk_start + chunk_size, num_pages)
# Pre-cache page pixmaps at low DPI for the current chunk
low_res_pixmaps = [doc.load_page(page_num).get_pixmap(dpi=low_dpi) for page_num in range(chunk_start, chunk_end)]
# Process each page in the current chunk
for page_num, low_res_pix in enumerate(low_res_pixmaps, start=chunk_start):
low_res_img = np.frombuffer(low_res_pix.samples, dtype=np.uint8).reshape(low_res_pix.height, low_res_pix.width, 3)
# Get bounding boxes from low DPI image
boxes = infer_image_and_get_boxes(low_res_img)
if boxes:
# Load high DPI image for cropping only if boxes are found
high_res_pix = doc.load_page(page_num).get_pixmap(dpi=high_dpi)
high_res_img = np.frombuffer(high_res_pix.samples, dtype=np.uint8).reshape(high_res_pix.height, high_res_pix.width, 3)
# Crop images at high DPI
cropped_imgs = crop_images_from_boxes(high_res_img, boxes, scale_factor)
all_cropped_images.extend(cropped_imgs)
return all_cropped_images
# Create Gradio interface
iface = gr.Interface(
fn=process_pdf,
inputs=gr.File(label="Upload a PDF"),
outputs=gr.Gallery(label="Cropped Figures and Tables from PDF Pages"),
title="Fast document layout analysis based on YOLOv8",
description="Upload a PDF file to get cropped figures and tables from each page."
)
# Launch the app
iface.launch()
|